Accurate intercensal estimates of energy access to track Sustainable Development Goal 7

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• dc.contributor.author Pokhriyal, Neeti
• dc.contributor.author Letouzé, Emmanuel
• dc.contributor.author Vosoughi, Soroush
• dc.date.accessioned 2023-05-12T06:24:05Z
• dc.date.available 2023-05-12T06:24:05Z
• dc.date.issued 2022
• dc.description.abstract Intercensal estimates of access to electricity and clean cooking fuels at policy planning microregions in a country are essential for understanding their evolution and tracking progress towards Sustainable Development Goals (SDG) 7. Surveys are prohibitively expensive to get such intercensal microestimates. Existing works, mainly, focus on electrification rates, make predictions at the coarse spatial granularity, and generalize poorly to intercensal periods. Limited works focus on estimating clean cooking fuel access, which is one of the crucial indicators for measuring progress towards SDG 7. We propose a novel spatio-temporal multi-target Bayesian regression model that provides accurate intercensal microestimates for household electrification and clean cooking fuel access by combining multiple types of earth-observation data, census, and surveys. Our model’s estimates are produced for Senegal for 2020 at policy planning microregions, and they explain 77% and 86% of variation in regional aggregates for electrification and clean fuels, respectively, when validated against the most recent survey. The diagnostic nature of our microestimates reveals a slow evolution and significant lack of clean cooking fuel access in both urban and rural areas in Senegal. It underscores the challenge of expanding energy access even in urban areas owing to their rapid population growth. Owing to the timeliness and accuracy of our microestimates, they can help plan interventions by local governments or track the attainment of SDGs when no ground-truth data are available.
• dc.format.mimetype application/pdf
• dc.identifier.citation Pokhriyal N, Letouzé E, Vosoughi S. Accurate intercensal estimates of energy access to track Sustainable Development Goal 7. EPJ Data Sci. 2022;11:60. DOI: 10.1140/epjds/s13688-022-00371-5
• dc.identifier.doi http://dx.doi.org/10.1140/epjds/s13688-022-00371-5
• dc.identifier.issn 2193-1127
• dc.identifier.uri http://hdl.handle.net/10230/56796
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof EPJ Data Science. 2022;11:60.
• dc.relation.isreferencedby https://github.com/neetip/energy_access
• dc.relation.isreferencedby https://static-content.springer.com/esm/art%3A10.1140%2Fepjds%2Fs13688-022-00371-5/MediaObjects/13688_2022_371_MOESM1_ESM.pdf
• dc.rights © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Clean energy access
• dc.subject.keyword Gaussian processes
• dc.subject.keyword Earth-observation data
• dc.subject.keyword Sustainable Development Goals
• dc.title Accurate intercensal estimates of energy access to track Sustainable Development Goal 7
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion